Reimplementation of paper Deep Painterly Harmonization

Pytorch implementation of paper "Deep Painterly Harmonization"

Official code written in Torch and lua can be found here Github Link

This PyTorch implementation follow the structure of Neural Style Pt Github Link

• Dependency

torch >= 1.3.1 
torhchvision >= 0.4.1 
python >= 3.7

python3 models/download_models.py

Before getting started with the code, checkout notebook/* to understand some implementation detail

• Run on Default Setting

By default

1. output image is stored under output directory
2. output image shape is 512
3. 0_target.jpg, 0_naive.jpg is used

python3 pass1.py

python3 pass2.py -output_img output/0_pass2_out.png

• Run Pass2 Starting from Offitial Pass1 result

python3 pass2.py -output_img output/0_pass2_out.png  \
								 -native_image official_result/0_inter_res.jpg

• Run on GPU setting like google colab

python3 pass1.py -gpu 0

python3 pass2.py -gpu 0

• Run on full detailed mode (if not specify, the program will run in silence mode)

python3 pass1.py -verbose

python3 pass2.py -verbose

• Run with specified style and content image

python3 pass1.py 
        -style_image ./data/1_target.jpg \
        -native_image ./data/1_naive.jpg \
        -tight_mask data/1_c_mask.jpg \
        -dilated_mask data/1_c_mask_dilated.jpg

• Run with specified iteration, lr, etc

python3 pass1.py -lr 1e-1 -p1_n_iters 2000 -p2_n_iters 1000 

• style_image, native_image, tight_mask, dilated_mask : specify which style & content image to transfer

• output_img : name for output image, defualt output/0_pass1_out.png

• output_img_size : output image size of the larger side

• optim, lr, n_iter : learning parameter choice

• print_interval : print loss interval

• save_img_interval : save intermediate image interval

• gpu : 'cpu' or '0'

• content_layers, style_layers : specify content layer, style layer

• content_weight, style_weight, tv_weight : weight for each loss, all style loss share the same weight

• model : model choice, 'vgg16' / 'vgg19'

• model_file : user specify weight for model, must match model choice

• match_patch_size : patch size for feature map matching, default 3 like paper

• mask_on : use mask or not, default mask on like paper, you can also choose mask off to compute mathing feature map for whole content image & compuet loss on whole content image

• log_on : use log or not, default log off

• log_file : file name to log

• verbose : if not specify, then the program will run in silence mode

Some dataset have been found that might work for the auxilary network (network that pick the weight of loss)

WikiArt from ArtGan - Github Link

• dataset download link

• annotation download link (use the style data part)

If you find this code useful for your research, please cite:

@misc{DPH2018,
  author = {Xiao Song, Ziyan Wang, Deyang Dai},
  title = {Deep-Painterly-Harmonization-in-PyTorch},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/XiaoSong9905/Deep-Painterly-Harmonization-in-PyTorch}},
}